Abstract

This study investigated the feasibility of automatic image registration of MR high-spatial resolution proximal femur trabecular bone images as well as the effects of gray-level interpolation and volume of interest (VOI) misalignment on MR-derived trabecular bone structure parameters. For six subjects in a short-term study, a baseline scan and a follow-up scan of the proximal femur were acquired on the same day. For ten subjects in a long-term study, a follow-up scan of the proximal femur was acquired after the baseline. An automatic image registration technique, based on mutual information, utilized a baseline and a follow-up scan to compute transform parameters that aligned the two images. In the short-term study, these parameters were subsequently used to transform the follow-up image with three different gray-level interpolators. Nearest-neighbor interpolation and -spline approximation did not significantly alter bone parameters, while linear interpolation significantly modified bone parameters . Improvement in image alignment due to the automatic registration for the long-term and short-term study was determined by inspecting difference images and 3D renderings. This work demonstrates the first application of automatic registration, without prior segmentation, of high-spatial resolution trabecular bone MR images of the proximal femur. Additionally, inherent heterogeneity in trabecular bone structure and imprecise positioning of the VOI along the slice (anterior–posterior) direction resulted in significant changes in bone parameters . Results suggest that automatic mutual information registration using -spline approximation or nearest neighbor gray-level interpolation to transform the final image ensures VOI alignment between baseline and follow-up images and does not compromise the integrity of MR-derived trabecular bone parameters used in this study.

Received 10 April 2008Revised 08 August 2008Accepted 13 August 2008Published online 22 September 2008

Acknowledgments:

This work was supported by NIH Grant No. ROI-AG017762 and ARCS and Evnin-Wright Fellowships. The authors would like to acknowledge Dr. Roland Krug for helping to acquire the high-spatial resolution MR images of trabecular bone of the proximal femur and Dr. David Newitt for providing the software for trabecular bone microarchitecture quantification.